Binary neural network
Binary neural network is an artificial neural network, where commonly used floating-point weights are replaced with binary ones.[1]
It saves storage and computation, and serves as a technique for deep models on resource-limited devices. Using binary values can bring up to 58 times speedup.[2] Accuracy and information capacity of binary neural network can be manually controlled.[3] Binary neural networks do not achieve the same accuracy as their full-precision counterparts, but improvements are being made to close this gap.
References
- ↑ Courbariaux, M.; Bengio, Y.; David, J.-P. (2015). "BinaryConnect: training deep neural networks with binary weights during propagation". NIPS. arXiv:1511.00363.
- ↑ Rastegari, M.; Ordonez, V.; Redmon, J.; Farhadi, A. (2016). "XNOR-Net: ImageNet classification using binary convolutional neural networks". ECCV. arXiv:1603.05279.
- ↑ Ignatov, D.; Ignatov, A. (2020). "Controlling information capacity of binary neural network". Pattern Recognition Letters. 138: 276–281. arXiv:2008.01438. Bibcode:2020PaReL.138..276I. doi:10.1016/j.patrec.2020.07.033. S2CID 220961716.